Abstract
This paper suggests the use of automatic topic modeling for large-scale corpora of privacy policies using unsupervised learning techniques. The advantages of using unsupervised learning for this task are numerous. The primary advantages include the ability to analyze any new corpus with a fraction of the effort required by supervised learning, the ability to study changes in topics of interest along time, and the ability to identify finer-grained topics of interest in these privacy policies. Based on general principles of document analysis we synthesize a cohesive framework for privacy policy topic modeling and apply it over a corpus of 4,982 privacy policies of mobile applications crawled from the Google Play Store. The results demonstrate that even with this relatively moderate-size corpus quite comprehensive insights can be attained regarding the focus and scope of current privacy policy documents. The topics extracted, their structure and the applicability of the unsupervised approach for that matter are validated through an extensive comparison to similar findings reported in prior work that uses supervised learning (which heavily depends on manual annotation of experts). The comparison suggests a substantial overlap between the topics found and those reported in prior work, and also unveils some new topics of interest.
Original language | English |
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Title of host publication | The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019 |
Publisher | Association for Computing Machinery, Inc |
Pages | 563-568 |
Number of pages | 6 |
ISBN (Electronic) | 9781450366755 |
DOIs | |
State | Published - 13 May 2019 |
Event | 2019 World Wide Web Conference, WWW 2019 - San Francisco, United States Duration: 13 May 2019 → 17 May 2019 |
Publication series
Name | The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019 |
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Conference
Conference | 2019 World Wide Web Conference, WWW 2019 |
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Country/Territory | United States |
City | San Francisco |
Period | 13/05/19 → 17/05/19 |
Bibliographical note
Publisher Copyright:� 2019 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY-NC-ND 4.0 License.
Funding
This research was partially supported by the ISRAEL SCIENCE FOUNDATION grant No. 1162/17.
Funders | Funder number |
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Israel Science Foundation | 1162/17 |
Keywords
- Privacy policies
- Topic modeling
- Unsuprevised learning